Biometric recognition was introduced as a more secure means of identity establishment. Biometric modalities are characteristics of the human body that are unique for every individual and that can be used to establish the identity of a person in a population. These characteristics can be either physiological or behavioral. For instance, the face, the iris and the fingerprints are physiological biometric modalities. Keystroke dynamics, the gait and the voice are examples of behavioral biometric modalities. The fact that biometric modalities are directly linked with individual users presents an opportunity to bridge the security gaps caused by traditional recognition strategies. Biometric modalities are difficult to steal or counterfeit when compared to PIN numbers or passwords. In addition, the convenience of not having to carry a piece of ID or remember a password can make biometric systems more accessible and easy to use.
An important consideration with regards to biometric technologies is the robustness to circumvention and replay attacks. Circumvention is a form of biometric forgery, for example such as falsified fingerprints that are reproduced from an original fingerprint. A replay attack is the presentation to the system of the original biometric feature from an illegitimate subject, for example such as pre-recorded voice playbacks in speaker recognition systems. Biometric obfuscation is another prominent risk, whereby biometric features are intentionally removed or damaged to avoid establishment of the true identity. For example, fingerprints can be intentionally altered to avoid identification. With the wide deployment of biometrics, these attacks are becoming frequent and concerns are being raised regarding the security levels that known biometric security technologies are capable of offering.
Concentrated efforts have been made to develop a next generation of biometric security technologies based on biometric characteristics that are inherently robust and that counter the above mentioned attacks. For example, in this pursuit, characteristics that are internal to the human body have been investigated, such as vein patterns and cognitive biometrics. Physiological signals constitute another category of new biometric modalities. Physiological signals encompass signals which are typically used in clinical diagnostics. Some examples of medical biometric signals are the electrocardiogram (ECG), phonocardiogram (PPG), electroencephalogram (EEG), blood volume pressure (BVP) and electromyogram (EMG).
A number of United States patents discuss biometric identification using physiological signals. The most commonly explored modality is the electrocardiogram (ECG). For example, U.S. Pat. No. 7,689,833 and U.S. Patent Application Publication No. 2010/0311482 present a method for the creation of a “grand-average” ECG signal, whereby users are identified based on how different they appear from the average.
U.S. Patent Application Publication No. 2004/0249294 discusses a similar idea for pre-determining an average feature vector, but in the frequency domain.
U.S. Patent Application Publication No. 2010/0090798 isolates and aligns pulse segments on ECG and PPG signals for biometric template design.
U.S. Pat. No. 7,630,521 discusses an artificial neural network (ANN) for the design of ECG biometric templates.
U.S. Pat. No. 7,796,01 describes a methodology for user authentication on smart-cards.
U.S. Patent Application Publication No. 2010/0113950 discusses user identification using cardiac signals on electronic devices with embedded sensors.
Various approaches to feature extraction for biometric recognition from ECG signals have been published in academic journals. These approaches can be categorized as either fiducial points dependent or independent, based on the type of features that comprise the biometric template. For example, in S. A. Israel, J. M. Irvine, A. Cheng, M. D. Wiederhold, and B. K. Wiederhold, “ECG to identify individuals,” Pattern Recognition, vol. 38, no. 1, pp. 133-142, 2005.”, a fiducial dependent methodology was proposed where the biometric template comprised of temporal characteristics of heart beats.
An academic publication K. S. Kim, T. H. Yoon, J. L., D. J. Kim, and H. S. Koo, “A robust human identification by normalized time-domain features of Electrocardiogram,” in Proceedings of 27th Annual Int. Conf on Eng. in Medicine and Biology Society, January 2005, pp. 1114-1117, proposed a method to normalize time domain features by Fourier synthesizing an up-sampled ECG heart beat.
A delineation method for particular ECG waveforms was proposed by Y. Singh and P. Gupta, “ECG to individual identification,” in Proceedings of IEEE Int. Conf. on Biometrics: Theory, Applications and Systems, October 2008, pp. 1-8.
Fiducial independent approaches have also been proposed. For example, G. Wübbeler, M. Stavridis, D. Kreiseler, R. D. Bousseljot, and C. Elster, “Verification of humans using the electrocardiogram,” Pattern Recogn. Lett., vol. 28, no. 10, pp. 1172-1175, 2007, combined different ECG leads into a two-dimensional heart vector which was used for biometric matching.
Can Ye, M. T. Coimbra, and B. V. K. V. Kumar, “Investigation of human identification using two-lead electrocardiogram (ECG) signals,” in Fourth IEEE International Conference on Biometrics: Theory Applications and Systems, September 2010, pp. 1-8, applied the discrete wavelet transform for ECG biometric recognition.
Another fiducial independent approach was discussed by N. Ghofrani and R. Bostani, “Reliable features for an ECG-based biometric system,” in Proceedings of 17th Iranian Conference of Biomedical Engineering, November 2010, pp. 1-5. This approach used an autoregressive model and the power spectral density of ECG segments for biometric matching.
Additional academic publications discussing relevant prior art include the following. F. Agrafioti, D. Hatzinakos, “ECG based recognition using second order statistics”, IEEE 6th Annual Conference on Communication Networks and Services Research, pp. 82-87, May 2008. This publication presented a method to ECG biometric feature extraction using the Autocorrelation (AC) and the Linear Discriminant Analysis (LDA).
F. Agrafioti and D. Hatzinakos, “Fusion of ECG sources for human identification,” in Third International Symposium on Communications, Control and Signal Processing (ISCCSP), Malta, March 2008, discusses a method to information fusion from various ECG leads which does not relate to the present invention.
F. Agrafioti, F. M. Bui, D. Hatzinakos, “On Supporting Anonymity in a BAN Biometric Framework”, 16th Int. Conf. on Digital Signal Processing, pp. 1-6, 2009; and “F. Agrafioti, F. M. Bui, and D. Hatzinakos, “Medical biometrics: The perils of ignoring time dependency,” in IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, Washington, DC, USA, September 2009, pp. 1-6; and F. Agrafioti, F. M. Bui, D. Hatzinakos, “Medical Biometrics in Mobile Health Monitoring”, Security and Communication Networks, Special Issue on Biometric Security for Mobile Computing, Wiley, vol. 4, no. 2, pp. 525-539, 2011. These publications discuss a biometric encryption solution for ECG biometric systems and a method for template updating.
F. Agrafioti, D. Hatzinakos, “Signal Validation for Cardiac Biometrics”, IEEE 35th Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 1734-1737, March 2010, that discusses signal processing.
F. Agrafioti and D. Hatzinakos, “ECG biometric analysis in cardiac irregularity conditions,” Signal, Image and Video Processing, pp. 1863-1703, 2008, that discusses robustness of the autocorrelation method to common cardiac disorders.
F. Agrafioti, J. Gao, D. Hatzinakos, “Heart Biometrics: Theory, Methods and Applications”, in Biometrics: Book 3, J. Yang, Eds., Intech, that is essentially a review of the relevant academic literature without new components in the method or framework for ECG biometric recognition.
Other relevant prior art journal articles include: F. Agrafioti, F. M. Bui, D. Hatzinakos, “Medical Information Management with ECG Biometrics: A Secure and Effective Framework”, in Handbook on Ambient Assisted Living for Healthcare, Well-being and Rehabilitation, Paul McCullagh, IOS Press; G. Kozmann, R. L. Lux, and L. S. Green, “Geometrical factors affecting the interindividual variability of the ECG and the VCG,” J. Electrocardiology, vol. 33, pp. 219-227, 2000; R. Hoekema, G. Uijen, and A. van Oosterom, “Geometrical aspect of the interindividual variaility of multilead ECG recordings,” IEEE Trans. Biomed. Eng., vol. 48, pp. 551-559, 2001; and H. Draper, C. Peffer, F. Stallmann, D. Littmann, and H. Pipberger, “The corrected orthogonal electrocardiogram and vectorcardiogram in 510 normal men (frank lead system),” Circulation, vol. 30, pp. 853-864, 1964.